Scaling and Similitude in Single Nozzle Supersonic Retropropulsion Aerodynamics Interference

Retropropulsion, or the firing of rocket engines or motors into the direction of flight, is a method of spacecraft deceleration and soft landing that dates back to the early 1960s. Current conceptual designs for landing humans on the surface of Mars require supersonic retropropulsion, or initiation of retropropulsion at supersonic freestream conditions, as part of an extended powered descent phase of flight. The objective of this work is to identify the design parameters and flow condition bounds for self-similar behavior of powered descent aerodynamic interference in relevant flight environments. In applications of sub-scale test data, an unknown uncertainty lies in scaling to and from full-scale environments and systems. The issue of scaling for the opposing flows characteristic of powered descent is the focus of the following analysis, using data from wind tunnel testing of figurations with a single, central nozzle as a point of departure

Voronoi diagrams as a visualization technique for spatial autocorrelation in point data sets

This thesis explores a new way of visualizing spatial autocorrelation in a GIS environment. It explores some relationships between spatial autocorrelation models, spatial interaction models and weighted Voronoi diagrams. Since the weighted Voronoi diagram is equivalent to a form of spatial interaction models, any GIS with the ability to generate a gravity model can be utilized to perform this new technique of exploratory spatial data analysis. This thesis demonstrates how the cross product form of‘ spatial autocorrelation models like the Geary and the Moran statistics is equivalent to the form of a multiplicatively weighted distance utilized in the definition of weighted Voronoi diagrams. A transformation of the multiplicatively weighted distance into a representation of the Geary or the Moran statistic can be used to generate different weighted Voronoi diagrams. Since such a representation incorporates the spatial variation of data points and the spatial variation of the attribute values assigned to the data points, it provides a more appropriate visual representation than do existing representations that only operate on the spatial distribution of the data points. Data sets with a known degree of spatial autocorrelation are created with a simultaneous autoregressive model. The behaviour of the visual representations of the Geary and the Moran statistics for spatial autocorrelation varying from a high positive to a high negative degree is examined

Fine-tuning implications for complementary dark matter and LHC SUSY searches

The requirement that SUSY should solve the hierarchy problem without undue fine-tuning imposes severe constraints on the new supersymmetric states. With the MSSM spectrum and soft SUSY breaking originating from universal scalar and gaugino masses at the Grand Unification scale, we show that the low-fine-tuned regions fall into two classes that will require complementary collider and dark matter searches to explore in the near future. The first class has relatively light gluinos or squarks which should be found by the LHC in its first run. We identify the multijet plus E_T^miss signal as the optimal channel and determine the discovery potential in the first run. The second class has heavier gluinos and squarks but the LSP has a significant Higgsino component and should be seen by the next generation of direct dark matter detection experiments. The combined information from the 7 TeV LHC run and the next generation of direct detection experiments can test almost all of the CMSSM parameter space consistent with dark matter and EW constraints, corresponding to a fine-tuning not worse than 1:100. To cover the complete low-fine-tuned region by SUSY searches at the LHC will require running at the full 14 TeV CM energy; in addition it may be tested indirectly by Higgs searches covering the mass range below 120 GeV.Comment: References added. Version accepted for publication in JHE

Functional Sequential Treatment Allocation

Consider a setting in which a policy maker assigns subjects to treatments, observing each outcome before the next subject arrives. Initially, it is unknown which treatment is best, but the sequential nature of the problem permits learning about the effectiveness of the treatments. While the multi-armed-bandit literature has shed much light on the situation when the policy maker compares the effectiveness of the treatments through their mean, much less is known about other targets. This is restrictive, because a cautious decision maker may prefer to target a robust location measure such as a quantile or a trimmed mean. Furthermore, socio-economic decision making often requires targeting purpose specific characteristics of the outcome distribution, such as its inherent degree of inequality, welfare or poverty. In the present paper we introduce and study sequential learning algorithms when the distributional characteristic of interest is a general functional of the outcome distribution. Minimax expected regret optimality results are obtained within the subclass of explore-then-commit policies, and for the unrestricted class of all policies

Conservative upper limits on WIMP annihilation cross section from Fermi-LAT γ\gamma-rays

The spectrum of an isotropic extragalactic $\gamma$-ray background (EGB) has been measured by the Fermi-LAT telescope at high latitudes. Two new models for the EGB are derived from the subtraction of unresolved point sources and extragalactic diffuse processes, which could explain from 30% to 70% of the Fermi-LAT EGB. Within the hypothesis that the two residual EGBs are entirely due to the annihilation of dark matter (DM) particles in the Galactic halo, we obtain $conservative$ upper limits on their annihilation cross section \sigmav. Severe bounds on a possible Sommerfeld enhancement of the annihilation cross section are set as well. Finally, would {\sigmav} be inversely proportional to the WIMP velocity, very severe limits are derived for the velocity-independent part of the annihilation cross section.Comment: Proceedings of XII Taup Conference, Munich, September 201

Tuning supersymmetric models at the LHC: A comparative analysis at two-loop level

We provide a comparative study of the fine tuning amount (Delta) at the two-loop leading log level in supersymmetric models commonly used in SUSY searches at the LHC. These are the constrained MSSM (CMSSM), non-universal Higgs masses models (NUHM1, NUHM2), non-universal gaugino masses model (NUGM) and GUT related gaugino masses models (NUGMd). Two definitions of the fine tuning are used, the first (Delta_{max}) measures maximal fine-tuning wrt individual parameters while the second (Delta_q) adds their contribution in "quadrature". As a direct result of two theoretical constraints (the EW minimum conditions), fine tuning (Delta_q) emerges as a suppressing factor (effective prior) of the averaged likelihood (under the priors), under the integral of the global probability of measuring the data (Bayesian evidence p(D)). For each model, there is little difference between Delta_q, Delta_{max} in the region allowed by the data, with similar behaviour as functions of the Higgs, gluino, stop mass or SUSY scale (m_{susy}=(m_{\tilde t_1} m_{\tilde t_2})^{1/2}) or dark matter and g-2 constraints. The analysis has the advantage that by replacing any of these mass scales or constraints by their latest bounds one easily infers for each model the value of Delta_q, Delta_{max} or vice versa. For all models, minimal fine tuning is achieved for M_{higgs} near 115 GeV with a Delta_q\approx Delta_{max}\approx 10 to 100 depending on the model, and in the CMSSM this is actually a global minimum. Due to a strong ($\approx$ exponential) dependence of Delta on M_{higgs}, for a Higgs mass near 125 GeV, the above values of Delta_q\approx Delta_{max} increase to between 500 and 1000. Possible corrections to these values are briefly discussed.Comment: 23 pages, 46 figures; references added; some clarifications (section 2